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Separation anxiety is a common behavior disorder, but our understanding of its etiology and underlying neurochemical mechanisms remains limited. The purpose of this study was to compare serum brain-derived neurotrophic factor (BDNF) levels in dogs with and without separation anxiety. BDNF, one of several neurotrophins, is prevalent in the limbic region and prefrontal cortex, which are key areas related to the regulation of mood, emotion, and cognition. Although an enriched environment increases BDNF levels, stress and glucocorticoid exposure decrease the expression of BDNF in the hippocampus and prefrontal cortex. As separation anxiety is associated with chronic stress, we hypothesized that BDNF levels in dogs affected by separation anxiety would be lower than BDNF levels in nonaffected dogs. A total of forty-eight dogs were classified by a board-certified behaviorist as affected (n = 15) or nonaffected (n = 33) by separation anxiety based on review of video clips of the dogs when left alone and owner reports of the dogs’ behavior. Groups did not differ in regard to age, body weight, body condition score, or sex, and most dogs were mixed breed. Fasted serum samples were analyzed with a canine BDNF ELISA. Nine (60%) dogs in the affected group and 12 (36%) dogs in the nonaffected group had a BDNF level

In 2017, the Korean government launched an unprecedentedly large-scaled latent tuberculosis infection (LTBI) screening project which covered more than a million individuals in congregate settings. A total of 1,047,689 participants of source population (n = 2,336,157) underwent LTBI testing from 2017 to 2018. The overall LTBI test uptake rate during this project was 44.8%. Workers in daycare centers (83.5%) and kindergartens (78.9%) showed high participation rate. A total of 1,012,206 individuals with valid results of interferon-gamma release assay (IGRA) were selected to constitute the IGRA cohort. Most of the enrolled participants in the IGRA cohort were in their working age. Approximately, three-quarters of total enrolled population were female. Investigating the LTBI prevalence, stages of LTBI care cascade, natural history of LTBI, efficacy of LTBI treatment and cost-effectiveness of LTBI screening are feasible within this IGRA cohort., Graphical Abstract

Humans spend approximately 90% of the daytime in buildings, and greenhouse gases (GHGs) emitted by buildings account for approximately 20% of total GHG emissions. As the energy consumed during building operation from a building life-cycle perspective amounts to approximately 70–90% of the total energy, it is essential to accurately predict the energy consumption of buildings for their efficient operation. This study aims to optimize a model for predicting the thermal energy consumption of buildings by (i) first extracting major variables through feature selection and deriving significant variables in addition to the collected data and (ii) predicting the thermal energy consumption using a machine learning model. Feature selection using random forest was performed, and 11 out of 17 available data were selected. The accuracy of the prediction model was significantly improved when the hour of day variable was added. The prediction model was constructed using an artificial neural network (ANN), and the improvement in the prediction accuracy was analyzed by comparing different cases of variable combinations. The ANN prediction accuracy was improved by 15% using the feature selection process compared to when all data were used as input data, and 25% coefficient of variation of the root mean square error (CVRMSE) accuracy was achieved.

Significance This report describes an opportunity for the bacterial cellulose nanofiber (BCNF) membrane, which has recently been attracting much attention due to its sustainability and high thermal stability, as a “single-layer,” good alternative membrane to the existing chemical-derived one in batteries, and thus allows us to find possible solutions to the environmental and safety issues. We achieved highest BCNF yield ever reported so far and fabricated a cylindrical lithium ion battery (LIB) by roll-to-roll process to realize remarkable cycle stability with 80% capacity retention even after 1,000 cycles, which is comparable to those of commercialized batteries. Finally, we introduced a Lewis base to improve thermal safety of the battery to a level comparable to that of a commercial ceramic-coated separator.